A number of methods are known for digitally encoding voice signal, that is, for sampling the signal and converting the flow of samples into a flow of bits, representing a binary encoding of the samples. This supposes that means are available for reconverting back the coded signal into its original analog form prior to providing it to its destination. Both coding and decoding operations generate distortions or noise to be minimized for optimizing the coding process.
Obviously, the highest the number of bits assigned to coding the signal, i.e. the bit rate, is, the better the coding would be. Unfortunately, due to cost efficiency requirements, like for instance cost of transmission channels, one needs concentrating several user sources of voice signals on a same transmission channel through multiplexing operations. Therefore, the lower the bit rate assigned to each voice coding, the better the system is. Consequently, one needs optimizing the coding quality and efficiency at any desired bit rate. A lot of efforts have been devoted to developing coding methods enabling optimizing the coding/decoding quality, or in other words, enabling minimizing the coding noise at a given rate.
A method was presented by M. Schroeder and B. Atal at the ICASSP 1985 with title "Code-Excited Linear Prediction (CELP); High-quality speech at very low bit rates" Basically, said method includes pre-storing several sets of coded data (codewords) into a code-book at known referenced locations within the book. The flow of samples of the voice signal to be encoded is then split into blocks of consecutive samples and then each block is represented by the reference of the codeword which matches best to it. A main drawback of this method is due to it involving a high computational complexing.
The method was further improved in "Fast CELP coding based on algebraic codes" presented by J. P Adoul et. al at ICASSP 1987, to enable lowering the "huge amount of computations involved". However, said computations still involve inverse filtering, i.e. rather highly computing power consumer, over each of the code-book codewords tested, for each block of signal samples to be encoded.